riga

import theano
from pylearn2.models import mlp
from pylearn2.training_algorithms import sgd
from pylearn2.termination_criteria import EpochCounter
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
import numpy as np
from random import randint


class XOR(DenseDesignMatrix):

riga

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
HipChat integration that parses the thecodinglove.com feed and sends
new posts to a HipChat room.

Dependencies:
requests, https://pypi.python.org/pypi/requests
feedparser, https://pypi.python.org/pypi/feedparser

riga

# coding: utf-8

"""
Test evaluation script for the DeepJet tagger.

Input dimensions and names from
https://github.com/cms-sw/cmssw/blob/02d4198c0b6615287fd88e9a8ff650aea994412e/RecoBTag/TensorFlow/plugins/DeepFlavourTFJetTagsProducer.cc
"""

import os

riga

#!/usr/bin/env bash

# install docker
yum remove -y docker docker-client docker-client-latest docker-common docker-latest docker-latest-logrotate docker-logrotate docker-engine
yum install -y yum-utils device-mapper-persistent-data lvm2
yum-config-manager --add-repo https://download.docker.com/linux/centos/docker-ce.repo
yum install -y docker-ce docker-ce-cli containerd.io
systemctl start docker

# enable autocompletion

riga

#!/usr/bin/env bash

action() {
    export SCRAM_ARCH="slc7_amd64_gcc820"
    export CMSSW_VERSION="CMSSW_11_0_0_pre9"

    if [ ! -d "$CMSSW_VERSION" ]; then
        scramv1 project CMSSW "$CMSSW_VERSION"

        cd "$CMSSW_VERSION/src"

riga

# coding: utf-8


import os


def fwlite_loop(path, handle_data=None, start=0, end=-1, object_type="Event"):
    """
    Opens one or more ROOT files defined by *path* and yields the FWLite event. When *handle_data*
    is not *None*, it is supposed to be a dictionary ``key -> {"type": ..., "label": ...}``. In that

riga

# coding: utf-8


import os
import tensorflow as tf


def read_constant_graph(graph_path, create_session=True, as_text=False):
    graph = tf.Graph()
    with graph.as_default():

riga

action() {
    local base="$( /bin/pwd )"

    git clone https://github.com/cms-sw/pkgtools.git
    git clone --single-branch --branch tf-2.0 https://github.com/riga/cmsdist.git

    ./pkgtools/cmsBuild -i tf-2.0 -c cmsdist/ --repository cms.week0 --arch slc7_amd64_gcc820 -j 10 build cmssw-tool-conf --no-cleanup

    export SCRAM_ARCH="slc7_amd64_gcc820"
    export CMSSW_VERSION="CMSSW_11_0_0_pre12"

riga

# coding: utf-8

"""
This example demonstrates the usage of the LBN different approaches:
  - TF eager mode
  - TF AutoGraph
  - Keras model / layer
"""

import numpy as np

riga

# coding: utf-8


import sys
import gc
import ctypes


# configuration of Py_IncRef and Py_DecRef
_c_inc_ref = ctypes.pythonapi.Py_IncRef